Automating customer care through self-service solutions (e.g., Interactive Voice Response (IVR), web-based self-care, etc.) results in substantial cost savings and operational efficiencies. However, due to several factors, such automated systems are unable to provide customers with a quality experience. Such factors include the highly constrained nature of automated interactions, poor error recovery in automated interactions, and poor context handling in automated interactions. The present invention addresses some of the deficiencies experienced with presently existing automated care systems.
One challenge in providing automated customer service (e.g., through an interactive voice response system) is a tradeoff between cost and customer satisfaction. While customer interactions which take place using an automated system (e.g., an interactive voice response system) are generally less expensive than interactions which take place between a customer and a human being, automated interactions are also likely to lead to lower customer satisfaction. One technique for addressing this problem is to provide customer service wherein an interaction initially takes place between a customer and an automated system, and, if the interaction seems to be approaching a negative outcome (i.e., “going bad”), transferring the interaction from an automated to a live interaction. However, an obstacle to the successful use of this technique is the problem of determining when an interaction is “going bad.” If an algorithm for determining when an interaction is “going bad” is oversensitive, then too few interactions will be completed using automation, resulting in unnecessary cost. If an algorithm for determining when an interaction is “going bad” is under-sensitive, then too few interactions will be transferred to a live interaction, resulting in lower customer satisfaction and, ultimately, a lost customer and lost business. Further, even creating an algorithm for determining whether an interaction is “going bad” can be a difficult task. The teachings of this application can be used to address some of these deficiencies in the state of the art for systems and methods used in customer interactions.